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Cover image for Email Outreach & Sentiment Detection Automation
Sabbir Ahmed Chowdhury
Sabbir Ahmed Chowdhury

Posted on • Edited on

Email Outreach & Sentiment Detection Automation

I’ve built an Email Outreach & Sentiment Detection Automation in n8n that completely streamlines the process of sending outreach emails, tracking replies, and analyzing sentiment with AI.

🔹 How it works

  1. Outreach Automation

The workflow kicks off on a daily schedule and selects 5–10 leads from a Google Sheet.

It loops through the selected contacts and sends out personalized outreach emails via Gmail.

Each sent email is logged back into Google Sheets with a status update (Sent) for full tracking.

  1. Reply Monitoring

A second workflow runs every 15 minutes to check for new unread replies.

It extracts the required fields (sender, subject, body, etc.) and checks if the email is indeed a reply to the outreach.

The content is cleaned up by removing quoted text, extra strings, and special characters so only the fresh reply remains.

  1. Sentiment Detection with AI

The cleaned reply is passed to an AI Agent powered by Google Gemini with Simple Memory and a Structured Output Parser.

The AI evaluates the reply and categorizes it (e.g., Positive, Neutral, Negative).

The result, along with the reply text, is logged back into Google Sheets with status = Replied.

🔑 Why this is powerful

✅ Outreach volume is controlled (5–10/day for natural delivery)
✅ Automated logging of sent & replied emails in Google Sheets
✅ AI-powered sentiment detection ensures quick triage of responses
✅ Scalable & hands-free – from sending, monitoring, cleaning, to analyzing replies

This automation essentially acts as a virtual SDR assistant — sending outreach, logging every action, and telling you instantly whether replies are positive or not.

Email Outreach & Sentiment Detection Automation

Top comments (1)

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paddy_m profile image
Paddy

Nice work—n8n is a great fit for stitching outreach + reply handling together.

A couple things that usually make sentiment automation more reliable in the wild:

  • Don’t just label “positive/negative.” Add intent buckets like interested, not now, unsubscribe, OOO, pricing request, wrong person. Those drive cleaner next steps.
  • Use a short thread summary + last message only for classification (full threads can confuse models).
  • Add guardrails: auto-suppress on “unsubscribe/stop,” detect OOO dates, and throttle follow-ups.
  • Log model confidence and route low-confidence replies to manual review.

If you ever want to pair this with lead sourcing/enrichment, our AI Lead generation solution can feed qualified contacts into n8n so the workflow starts with better targets.

Check out signalscout.live

It's currently free to use